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Kinetic modeling of microbial reactions is a common tool for addressing the central environmental questions of our time, from contaminant remediation to the global carbon cycle. This review presents an overview of trait-based frameworks for modeling the kinetics of microbial reactions, with an emphasis on environmental application. I first highlight two key model assumptions: the simplification of microbial communities as ensembles of microbial functional groups and the description of microbial metabolism at a coarse-grained level with three metabolic reactions – catabolic reaction, biomass synthesis, and maintenance. Next, I aim to establish a connection between microbial rate laws and the mechanisms of metabolic reactions. For metabolic reactions limited by single substrates, the widely used rate law is the Monod equation. However, in cases where substrates are solids or nonaqueous phase liquids (NAPLs), the Contois equation and the Best equation may offer better alternatives. In microbial metabolisms limited by multiple nutrients simultaneously, two competing rate laws exist: the multiplicative rate law and Liebig’s law of the minimum. Then I present two strategies for extending the modeling framework developed for laboratory cultures to natural environments. One strategy follows the multiplicative rate law and incorporates dimensionless functions to account for pH, temperature, salinity, cell density, and other environmental conditions. The other strategy focuses on the physiology of natural microbes, explicitly considering dormancy, biomass decay, and physiological acclimation. After that, I highlight recent improvements enabled by the application of molecular biology tools, ranging from functional gene-based models to metabolic models. Finally, I discuss the inherent limitations of trait-based modeling frameworks and their implications for model development and evaluation, including the gap between functional groups represented in silico and microbial communities found in natural environments, as well as the requirement of internal consistency in microbial parameter sets. 

Model of hydrogenotrophic methanogenesis explains how enzyme level activity determines methane isotopic composition as function. 

Chemotrophic microorganisms face the steep challenge of limited energy resources in natural environments. This observation has important implications for interpreting and modeling the kinetics and thermodynamics of microbial reactions. Current modeling frameworks treat microbes as autocatalysts, and simulate microbial energy conservation and growth with fixed kinetic and thermodynamic parameters. However, microbes are capable of acclimating to the environment and modulating their parameters in order to gain competitive fitness. Here we constructed an optimization model and described microbes as self-adapting catalysts by linking microbial parameters to intracellular metabolic resources. From the optimization results, we related microbial parameters to the substrate concentration and the energy available in the environment, and simplified the relationship between the kinetics and the thermodynamics of microbial reactions.We took as examples Methanosarcina and Methanosaeta – the methanogens that produce methane from acetate – and showed how the acclimation model extrapolated laboratory observations to natural environments and improved the simulation of methanogenesis and the dominance of Methanosaeta over Methanosarcina in lake sediments. These results highlight the importance of physiological acclimation in shaping the kinetics and thermodynamics of microbial reactions and in determining the outcome of microbial interactions. 

ABSTRACT The Monod equation has been widely applied as the general rate law of microbial growth, but its applications are not always successful. By drawing on the frameworks of kinetic and stoichiometric metabolic models and metabolic control analysis, the modeling reported here simulated the growth kinetics of a methanogenic microorganism and illustrated that different enzymes and metabolites control growth rate to various extents and that their controls peak at either very low, intermediate, or very high substrate concentrations. In comparison, with a single term and two parameters, the Monod equation only approximately accounts for the controls of rate-determining enzymes and metabolites at very high and very low substrate concentrations, but neglects the enzymes and metabolites whose controls are most notable at intermediate concentrations. These findings support a limited link between the Monod equation and methanogen growth, and unify the competing views regarding enzyme roles in shaping growth kinetics. The results also preclude a mechanistic derivation of the Monod equation from methanogen metabolic networks and highlight a fundamental challenge in microbiology: single-term expressions may not be sufficient for accurate prediction of microbial growth. IMPORTANCE The Monod equation has been widely applied to predict the rate of microbial growth, but its application is not always successful. Using a novel metabolic modeling approach, we simulated the growth of a methanogen and uncovered a limited mechanistic link between the Monod equation and the methanogen’s metabolic network. Specifically, the equation provides an approximation to the controls by rate-determining metabolites and enzymes at very low and very high substrate concentrations, but it is missing the remaining enzymes and metabolites whose controls are most notable at intermediate concentrations. These results support the Monod equation as a useful approximation of growth rates and highlight a fundamental challenge in microbial kinetics: single-term rate expressions may not be sufficient for accurate prediction of microbial growth. 

The Monodequationhasbeenwidelyappliedasthegeneralratelaw of microbialgrowth,butitsapplicationsarenotalwayssuccessful.Bydrawingon the frameworksofkineticandstoichiometricmetabolicmodelsandmetaboliccon- trol analysis,themodelingreportedheresimulatedthegrowthkineticsofametha- nogenic microorganismandillustratedthatdifferentenzymesandmetabolitescon- trol growthratetovariousextentsandthattheircontrolspeakateitherverylow, intermediate, orveryhighsubstrateconcentrations.Incomparison,withasingle term andtwoparameters,theMonodequationonlyapproximatelyaccountsforthe controls ofrate-determiningenzymesandmetabolitesatveryhighandverylow substrate concentrations,butneglectstheenzymesandmetaboliteswhosecontrols are mostnotableatintermediateconcentrations.These findings supportalimited link betweentheMonodequationandmethanogengrowth,andunifythecompet- ing viewsregardingenzymerolesinshapinggrowthkinetics.Theresultsalsopre- clude amechanisticderivationoftheMonodequationfrommethanogenmetabolic networks andhighlightafundamentalchallengeinmicrobiology:single-termexpres- sions maynotbesufficient foraccuratepredictionofmicrobialgrowth. 

The Q10 coefficient is the ratio of reaction rates at two temperatures 10°C apart, and has been widely applied to quantify the temperature sensitivity of organic matter decomposition. However, biogeochemists and ecologists have long recognized that a constant Q10 coefficient does not describe the temperature sensitivity of organic matter decomposition accurately. To examine the consequences of the constant Q10 assumption, we built a biogeochemical reaction model to simulate anaerobic organic matter decomposition in peatlands in the Upper Peninsula of Michigan, USA, and compared the simulation results to the predictions with Q10 coefficients. By accounting for the reactions of extracellular enzymes, mesophilic fermenting and methanogenic microbes, and their temperature responses, the biogeochemical reaction model reproduces the observations of previous laboratory incubation experiments, including the temporal variations in the concentrations of dissolved organic carbon, acetate, dihydrogen, carbon dioxide, and methane, and confirms that fermentation limits the progress of anaerobic organic matter decomposition. The modeling results illustrate the oversimplification inherent in the constant Q10 assumption and how the assumption undermines the kinetic prediction of anaerobic organic matter decomposition. In particular, the model predicts that between 5°C and 30°C, the decomposition rate increases almost linearly with increasing temperature, which stands in sharp contrast to the exponential relationship given by the Q10 coefficient. As a result, the constant Q10 approach tends to underestimate the rates of organic matter decomposition within the temperature ranges where Q10 values are determined, and overestimate the rates outside the temperature ranges. The results also show how biogeochemical reaction modeling, combined with laboratory experiments, can help uncover the temperature sensitivity of organic matter decomposition arising from underlying catalytic mechanisms.